Domain Generalization
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The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain
Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/wonderseven/lssae
공식 코드Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder
https://github.com/workerbcd/dct
공식 코드https://github.com/wufan-cse/g-nas
공식 코드G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection
https://github.com/wyf0912/LDDG
공식 코드Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization
https://github.com/wyharveychen/CloserLookFewShot
공식 코드https://github.com/wzell/funcregr4domgen
공식 코드https://github.com/xarwinm/competence_estimation
공식 코드https://github.com/xavierohan/AdaClust_DomainBed
공식 코드https://github.com/xc-zhonghit/eeg-dg
공식 코드EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification
https://github.com/xhz0809/openstance
공식 코드https://github.com/xhz0809/x-shot
공식 코드https://github.com/xingchenzhao/DANNCE
공식 코드Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improve
https://github.com/xingchenzhao/MixDANN
공식 코드Robust White Matter Hyperintensity Segmentation on Unseen Domain
https://github.com/xingyu-group/3d-density-resampling-sdg
공식 코드Domain Generalization of 3D Object Detection by Density-Resampling
https://github.com/xjtushujun/slem-theory
공식 코드Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
https://github.com/xmed-lab/GenericSSL
공식 코드Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
https://github.com/xmed-lab/triplee-dg
공식 코드https://github.com/xpq-tech/keti
공식 코드Identifying Knowledge Editing Types in Large Language Models
https://github.com/xq141839/nusegdg
공식 코드https://github.com/xuzhez/mapseg
공식 코드https://github.com/xxgege/StableNet
공식 코드https://github.com/xxgege/nico-plus
공식 코드NICO++: Towards Better Benchmarking for Domain Generalization
https://github.com/xxxupeng/adl
공식 코드https://github.com/yafuly/CoGnition
공식 코드On Compositional Generalization of Neural Machine Translation
https://github.com/yasserben/clouds
공식 코드Collaborating Foundation Models for Domain Generalized Semantic Segmentation
https://github.com/yawencui/cmoa
공식 코드Generalized Few-Shot Continual Learning with Contrastive Mixture of Adapters
https://github.com/ybzh/efdm
공식 코드Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization
https://github.com/ycq091044/manydg
공식 코드ManyDG: Many-domain Generalization for Healthcare Applications
https://github.com/yfzhang114/Environment-Label-Smoothing
공식 코드Free Lunch for Domain Adversarial Training: Environment Label Smoothing
https://github.com/yfzhang114/adanpc
공식 코드Domain-Specific Risk Minimization for Out-of-Distribution Generalization
https://github.com/yifangao112/desam
공식 코드DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation
https://github.com/yihuacheng/puregaze
공식 코드PureGaze: Purifying Gaze Feature for Generalizable Gaze Estimation
https://github.com/yingyichen-cyy/JigsawViT
공식 코드https://github.com/yinjunbo/ssda3d
공식 코드SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud
https://github.com/yjyddq/DLIF
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.